Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

25 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)70-78
Journal / PublicationMicroelectronics Reliability
Volume70
Early online date9 Feb 2017
Publication statusPublished - Mar 2017

Abstract

Some lithium-ion battery materials show two-phase degradation behavior with evident inflection points, such as lithium nickel manganese cobalt oxide (Li(NiMnCo)O2 or NMC) cells. A model-based Bayesian approach is proposed in this paper to predict remaining useful life (RUL) for these types of batteries. First, a two-term logarithmic model is developed to capture the degradation trends of NMC batteries. By fitting the battery degradation data, it is experimentally demonstrated that the developed model is superior to existing empirical battery degradation models. A particle filtering–based prognostic method is then incorporated into the model to estimate the batteries' possible degradation trajectories. Correspondingly, the RUL values of NMC batteries are expressed in terms of probability density function. The effectiveness of the developed method is verified with our collected experimental data. The results indicate that the proposed prognostic method can achieve higher predictive accuracy than the existing two-term exponential model.

Research Area(s)

  • Degradation modeling, Lithium-ion batteries, Particle filtering, Remaining useful life prediction